chapter 16 Master Data Management
The Data Ecosystem: Data Quality
Data quality aims to ensure data are correct, complete, current, and consistent. It is possible to have data quality without DM, but it is not possible to have data DM without data quality. MDM efforts focus the costs and challenges of data quality on the core data.
Better information
Improves compliance reporting, generates operational efficiencies, and achieves competitive differentiation.
Factors Increasing the Data Management Challenge
Increased storage capabilities Layers of "enterprise" solutions Multiple groups managing data Ownership issues Short term workarounds
The Data Ecosystem: IM Strategy and Principles
Information Management (IM) covers all forms of information needed and produced by the business. The IM strategy and principles structure, secure, and improve information assets. IM strategy and principles provide the context in which MDM is accomplished
Develop an enterprise information policy
It is essential to delineate the principles around issues such as corporate data objectives, data ownership and accountability, privacy, security, and risk management.
What is Master Data Management (MDM)?
MDM is an application-independent process that describes, owns, and manages core business data entities. MDM ensures the consistency and accuracy of these data by providing a single set of guidelines for their management and thereby creates a common view of key data.
The Data Journey
Stage 1. "I admit I've got data, so I'll inventory it." Stage 2. "Let's identify what data is used by which application and processes. I'll discuss the role of information and ownership." Stage 3. "Let's limit how much data we move around and maybe design some information exchange requirements."
The Data Ecosystem: Enterprise Architecture
The IM strategy and principles should be important contributors to the enterprise architecture. Information and architecture should be as separate as possible. The establishment of a dialogue and discipline for core corporate data will provide the highest value.
The Data Management Challenge
The IT landscape is littered with legacy, packaged and developed applications, coupled with unstructured data. The uncontrolled silos of data make managing information very difficult and limit its strategic value.
The Data Ecosystem: Data Integration
The goal of data integration is to create a data warehouse as a credible source of integrated information. Data integration serves two purposes: Enables data to be combined and collected in a warehouse. Consolidates data that are not deemed to be core but which are created and updated by several applications.
Cost savings
Two main costs can be avoided by better DM: -- Costs caused by poor data quality (e.g., need to verify data, poor decisions). -- Costs caused by assuring data quality (e.g., prevent, detect, or repair poor data).
Prerequisites for MDM Success
1.Develop an enterprise information policy 2.Business ownership 3.Governance 4.The role of IT
Governance
A cross-functional and collaborative IT and business data governance process should be established. "MDM can't be sustained without governance".
The MDM Value Proposition
A single source of a company's data provides: Better information Cost savings Improved business capabilities Improved technical capabilities
Improved technical capabilities
A single source of data can eliminate data redundancy and facilitate integration. MDM is the prerequisite of a service-oriented architecture.
Improved business capabilities
A single source of data can improve customer service (e.g., ensuring customer privacy) and support flexibility (e.g., supporting globalization, acquisitions).
Business ownership
All stakeholders must be involved in MDM or political problems will likely ensue (e.g., executive and business sponsorship, data stewards, change management specialists).
The role of IT
DM is primarily a nontechnical problem; however technology and IT staff play important roles: -- IT staff has the skills to develop a data strategy, model the data, and assess applications. -- Technology maintains data models and repositories.
The Data Ecosystem: Data Management
Data management (DM) is the critical work of making decisions about data. Information stewards are responsible for DM and check the accuracy, timelines, life cycle, and redundancy of the data. MDM is a subset of DM that focuses on core data.
